Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies. (April 2017)

• Record Type: Journal Article
• Authors: Onay, Aytun; Onay, Melih; Abul, Osman
• Is Part Of: Computer methods and programs in biomedicine. Volume 142(2017)
• Page Start: 9
• Page End: 19
• Publication Date: 2017-04
• Subjects: Machine learning -- Support vector machine -- Drug discovery -- ToxPrint chemotypes -- Approved & withdrawn drug; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.02.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The machine learning classification methods to distinguish approved drugs from withdrawn ones in nervous system drugs. Support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Toxprint chemotype features of the drugs for various diseases. Chi-square attribute selection method for dimensionality reduction of drug descriptors. Determination of a set of discriminative fragments in nervous system withdrawn drug data sets by the ParMol package including gSpan algorithm. Abstract: Background and objectives: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. Methods: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. Results: According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N, N-dimethylethylamine, crotylamine, 5-methyl-2, 4-heptadiene, octatriene and carbonyl group. Conclusion: This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs.